GENIE: Watermarking Graph Neural Networks for Link Prediction

arXiv:2406.04805 · cs.CR, cs.LG · Submitted 2024-06-07 · Read on arXiv

cs.CR, cs.LG

Submitted: 2024-06-07

Updated: 2026-09-01

Terminology

• Pruning — 53× in this paper · explained in 4 episodes
• AUC — 43× in this paper · explained in 2 episodes
• Fine-Tuning — 30× in this paper · explained in 5 episodes
• Robustness — 26× in this paper · explained in 5 episodes
• GNNs — 24× in this paper · explained in 2 episodes
• Quantization — 16× in this paper · explained in 12 episodes
• Distillation — 15× in this paper · explained in 2 episodes
• Link Prediction — 13× in this paper
• Verification — 12× in this paper · explained in 2 episodes
• Deep Neural Network — 8× in this paper
• Knowledge Distillation — 7× in this paper · explained in 11 episodes
• Core — 5× in this paper · explained in 2 episodes
• Adjacency Matrix — 4× in this paper · explained in 1 episode
• Intellectual Property — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 3× in this paper · explained in 7 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Ownership Demonstration — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Dynamic Watermark Thresholding — 2× in this paper
• Generalizability — 2× in this paper · explained in 6 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adversarial Model — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Area Under the ROC Curve — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• False Negative Rate — 1× in this paper
• False Positive Rate — 1× in this paper
• Federated Learning — 1× in this paper · explained in 15 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tune All Layers — 1× in this paper
• Fine-Tune Last Layer — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Mean Squared Error — 1× in this paper
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Multiple-Gradient Descent Algorithm — 1× in this paper
• Re-Train All Layers — 1× in this paper
• Re-Train Last Layer — 1× in this paper
• Receiver Operating Characteristic — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Smoothed Bootstrap Approach — 1× in this paper

Sources

• Adversarial Model Extraction on Graph Neural Networks
• MEA-Defender: A Robust Watermark against Model Extraction Attack
• Effective backdoor attack on graph neural networks in link prediction tasks
• Semi-Supervised Classification with Graph Convolutional Networks
• Distilling the Knowledge in a Neural Network
• Kernel Density Estimation Bias under Minimal Assumptions

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